Variation in Cannabinoid Metabolites Present in the Urine of Adults Using Medical Cannabis Products in Massachusetts

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Research Letter | Substance Use and Addiction Variation in Cannabinoid Metabolites Present in the Urine of Adults Using Medical Cannabis Products in Massachusetts

Jodi M. Gilman, PhD; William A. Schmitt, AB; Grace Wheeler, BA; Randi M. Schuster, PhD; Jost Klawitter, PhD; Cristina Sempio, PhD; A. Eden Evins, MD, MPH

Introduction Author affiliations and article information are listed at the end of this article. A growing market of medical cannabis products claim to have specific Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) content, but regulation of THC and CBD content is inconsistent across states and generally weak.1 To examine the association between medical cannabis product use and exposure to THC and CBD, we quantified levels of THC, CBD, and their metabolites in urine of participants in a clinical trial (ClinicalTrials.gov identifier NCT03224468) of medical cannabis in Massachusetts.

Methods

This cohort study was approved by the Partners Human Research Committee. All participants provided written informed consent. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Adults (aged 18-65 years) with a desire to use cannabis for depression, pain, or insomnia were recruited through advertising and were assessed at baseline and 2, 4, 12, 24, and 48 weeks after initiating cannabis. The study took place between June 2017 and August 2020. At each visit, participants provided a urine sample, reported recency of cannabis use, and reported whether their primary products were THC-dominant, CBD-dominant, or approximately equal CBD and THC.

Table 1. Cannabinoid Metabolite Measurements in 256 Urine Samples From 97 Participants

Limit of Samples with detectable Concentration, Metabolite quantitation, ng/mL metabolite, No. (%) median (range), ng/mLa,b THC metabolites THC-COO-Gluc 7.8-2000 209 (81.6) 40.6 (<7.8-22353.6) THC-COOH 0.39-400 136 (53.1) 0.5 (<0.39-407.6) THC-Gluc 0.78-200 64 (25.0) <0.78 (<0.78-182.2) THCV-COOH 0.78-400 25 (11.1) <0.78 (<0.78-43.5) 11-OH-THC 1.56-400 10 (3.9) <1.56 (<1.56-3.9) THC 0.78-400 0 NA THCV 0.78-400 0 NA Any THC metabolitec NA 209 (81.6) NA CBD metabolites CBD-Gluc 0.78-100 116 (45.3) <0.78 (<0.78-215.6) 7-CBD-COOH 0.78-400 43 (16.8) <0.78 (<0.78-28.8) 7-OH-CBD 3.13-400 28 (10.9) <3.13 (<3.13-44.6) 6α-OH-CBD 1.56-400 10 (3.9) <1.56 (<0.78-9) 6βb-OH-CBD 0.78-400 9 (3.5) <0.78 (<0.78-7.4) CBD 0.78-400 1 (0.4) <0.78 (<0.78-1.1) CBDV 0.39-400 0 NA Any CBD metabolited NA 142 (55.5) NA

(continued)

Open Access. This is an open access article distributed under the terms of the CC-BY License.

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Table 1. Cannabinoid Metabolite Measurements in 256 Urine Samples From 97 Participants (continued)

Limit of Samples with detectable Concentration, Metabolite quantitation, ng/mL metabolite, No. (%) median (range), ng/mLa,b Other metabolites CBC 1.56-400 1 (0.4) <1.56 (<1.56-0.9) CBG 0.39-400 0 NA CBN 0.78-400 0 NA Any cannabinoid metabolite (sum) NA 220 (85.9) NA No cannabinoid metabolites detected NA 36 (14.0) NA

Abbreviations: 11-OH-THC, 11-hydroxy-Δ9-tetrahydrocannabinol; 6α-OH-CBD, 6-alpha-hydroxy-cannabidiol; 6β-OH-CBD, 6-beta-hydroxy-cannabidiol; 7-OH-CBD, 7-hydroxy-cannabidiol; 7-CBD-COOH, (3R-trans)-cannabidiol-7-oic acid; CBC, cannabichromene; CBD, cannabidiol; CBD-Gluc, cannabidiol glucuronide; CBDV, cannabidivarin; CBG, cannabigerol; CBN, cannabinol; NA, not applicable; THC-COO-Gluc, 1-nor-Δ9-tetrahydrocannabinol-9-carboxylic acid glucuronide; THC-COOH, 11-Nor-9-carboxy- Δ9-tetrahydrocannabinol; THCV-COOH, 11-nor-9-carboxy-Δ9-tetrahydrocannabivarin; THC-Gluc, Δ9-tetrahydrocannabinol glucuronide; THCV, Δ9-tetrahydrocannabivarin. a When calculating the median, concentrations falling below the lower limit of quantification were imputed as the lower limit of quantification. b Values above the upper limit of quantification are estimates. c In total, 72% of participants had detectable THC metabolites at all visits, and 9% had undetectable THC metabolites at all visits; 19% of participants were variable, with detectable THC metabolites at some visits but not others. There was no association between frequency of use and presence of THC metabolites (Kendall τ = 0.09; P = .09). d In total, 22% of participants had detectable CBD metabolites at all visits, and 19% of participants had undetectable CBD metabolites at all visits; 59% of participants were variable, with detectable CBD metabolites at some visits but not others. There was an association between frequency of use and presence of CBD metabolites (Kendall τ = 0.21; P < .001). THCV-COOH only has values for 224 samples because of a lack of reference THCV-COOH in 1 batch.

Table 2. Presence of CBD or THC Metabolites by Self-reported CBD-THC Content and Primary Route of Administration

Metabolite detected, samples, No. (%) CBD THC Self-report category Total samples, No. Yes No. Yes No Neither All product types CBD dominant 33 23 (69.7) 10 (30.3) 26 (78.8) 7 (21.2) 5 (15.2) Equal CBD and THC 54 34 (63.0) 20 (37.0) 35 (64.8) 19 (35.2) 13 (24.1) THC dominant 119 63 (52.9) 56 (47.1) 106 (89.1) 13 (10.9) 11 (9.2) Not sure or data not provided 50 22 (44.0) 28 (56.0) 42 (84.0) 8 (16.0) 7 (14.0) By route of administration Vapeda 137 67 (48.9) 70 (51.1) 101 (73.7) 36 (26.3) 27 (19.7) CBD dominant 11 9 (81.8) 2 (18.2) 10 (90.9) 1 (9.1) 1 (9.1) Equal CBD and THC 40 24 (60.0) 16 (40.0) 23 (57.5) 17 (42.5) 11 (27.5) THC dominant 58 25 (43.1) 33 (56.9) 47 (81.0) 11 (19.0) 9 (15.5) Not sure or data not provided 28 9 (32.1) 19 (67.9) 21 (75.0) 7 (25.0) 6 (21.4) Oralb 66 49 (74.2) 17 (25.8) 60 (90.9) 6 (9.1) 4 (6.1) CBD dominant 20 14 (70.0) 6 (30.0) 15 (75.0) 5 (25.0) 3 (15.0) Equal CBD and THC 10 9 (90.0) 1 (10.0) 10 (100.0) 0 0 THC dominant 28 19 (67.9) 9 (32.1) 27 (96.4) 1 (3.6) 1 (3.6) Not sure or data not provided 8 7 (87.5) 1 (12.5) 8 (100.0) 0 0 Smokedc 53 26 (49.1) 27 (50.9) 48 (90.6) 5 (9.4) 5 (9.4) CBD dominant 2 0 2 (100.0) 1 (50.0) 1 (50.0) 1 (50.0) Equal CBD and THC 4 1 (25.0) 3 (75.0) 2 (50.0) 2 (50.0) 2 (50.0) THC dominant 33 19 (57.6) 14 (42.4) 32 (97.0) 1 (3.0) 1 (3.0) Not sure or data not provided 14 6 (42.9) 8 (57.1) 13 (92.9) 1 (7.1) 1 (7.1)

Abbreviations: CBD, cannabidiol; THC, Δ9-tetrahydrocannabinol. metabolites and dosage (Kendall τ = 0.01; P = .90), but a significant association a The most commonly reported measure of dose was in “hits” (134 hits; mean, 3.2 hits; between the presence of THC metabolites and dosage (Kendall τ = −0.36; P = .01). range, 1-25 hits). For vaped cannabis measured in hits, there was no association c The most commonly reported measure of dose was in hits (39 hits; mean, 4.9 hits; between the presence of CBD metabolites and dosage (Kendall τ = 0.07; P = .36) or range, 1-15 hits) or joints (7 joints; mean, 1.2 joints; range, 0.5-3.5 joints). For smoked the presence of THC metabolites and dosage (Kendall τ = 0.03; P = .71). cannabis measured in hits, there was an association between the presence of CBD b The most commonly reported measure of dose was in milligrams (40 doses; mean, 8.4 metabolites and dosage (Kendall τ = 0.44; P = .001), as well as the presence of THC mg; 2-25 mg) or drops (8 doses; mean, 4.4 drops; range, 1-10 drops). For oral cannabis metabolites and dosage (Kendall τ = 0.40; P = .004). measured in milligrams, there was no association between presence of CBD

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Samples collected during visits in which participants reported using products from licensed dispensaries within the prior 48 hours and at least 3 to 4 days per week since the previous visit were analyzed using high-performance liquid chromatography with tandem mass spectrometry.2 We ran separate models for THC and CBD metabolites using Kendall rank correlations for ordinal variables and logistic regressions for nominal variables, using R statistical software version 4.0 (R Project for Statistical Computing). All tests and 95% CIs were 2-sided, and significance was defined as P < .05. For logistic regressions, we performed a Wald test. Data analysis was performed from September 2020 to February 2021.

Results

Ninety-seven participants (mean [SD] age, 39.6 [14.74] years; 65 women [67.01%]) provided 256 urine samples meeting the criteria for analysis. Participants were light users at baseline (53% used less than monthly). After baseline, 39% to 47% used 3 to 4 days per week, 15% to 20% used 5 to 6 days per week, and 29% to 45% used daily. At least 1 cannabis metabolite was detected in 220 samples (85.9%) (Table 1). Among participants who reported using CBD-dominant or equal CBD-THC products, there was no detectable CBD metabolite in 10 samples (30.3%) and 20 samples (37.0%), respectively (Table 2). THC was detected in 26 samples (78.8%) from participants reporting use of CBD-dominant products. Among samples from participants reporting THC-dominant or equal CBD-THC products, no THC metabolites were present in 13 samples (10.9%) and 19 samples (35.2%), respectively. Although vaping was the most common method of administration, 27 samples (19.7%) from participants who reported vaping contained no measurable cannabinoid whatsoever. CBD metabolites were more likely to be detected in participants who used oral than vaped (odds ratio [OR], 3.01; 95% CI, 1.58-5.74; P < .001) or smoked (OR, 2.99; 95% CI, 1.38-6.47; P = .005) products. THC metabolites were more likely to be detected in participants who used oral (OR, 3.56; 95% CI, 1.42-8.96; P = .007) and smoked (OR, 3.42; 95% CI, 1.26-9.27; P = .02) products than in vaped products.

Discussion

Among adults using medical cannabis frequently and recently, THC and CBD metabolite concentrations in urine often differed from expected exposure. Approximately one-third of samples from people reporting using CBD-dominant products contained no measurable CBD metabolite. Nearly 1 in 5 samples from those using vaped cannabis contained no detectable cannabinoids. There were no dose-metabolite associations for vaped products. This may indicate that vaping devices may not heat cannabis products appropriately, and US Food and Drug Administration–approved devices may deliver more consistent cannabinoid exposure. Product and delivery method variability present challenges to assessing the efficacy and safety of medical cannabis. Methodological limitations of this study include participant-determined doses, possible errors in self-report, no analysis of the cannabis products themselves, and individual differences in rate of absorption and metabolism. Products were purchased in Greater Boston dispensaries, so results may not generalize to regions with different regulations. The findings of this cohort study are consistent with those of a study1 of cannabis products purchased in California and Washington, in which more than one-half of products were incorrectly labeled. These findings indicate that adults using medical cannabis products may have incomplete or incorrect information regarding expected cannabinoid exposure from these purchased products, impeding informed patient choice and investigation of pharmacologic and therapeutic properties of cannabis products.

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ARTICLE INFORMATION Accepted for Publication: February 21, 2021. Published: April 12, 2021. doi:10.1001/jamanetworkopen.2021.5490 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Gilman JM et al. JAMA Network Open. Corresponding Author: Jodi M. Gilman, PhD, Center for Addiction Medicine, Department of Psychiatry, Massachusetts General Hospital, 101 Merrimac St, Ste 320, Boston, MA 02114 ([email protected]). Author Affiliations: Center for Addiction Medicine, Department of Psychiatry, Massachusetts General Hospital, Boston (Gilman, Schmitt, Wheeler, Schuster, Evins); Harvard Medical School, Boston, Massachusetts (Gilman, Schuster, Evins); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts (Gilman); Department of Anesthesiology, University of Colorado, Aurora (Klawitter, Sempio). Author Contributions: Dr Gilman had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Gilman, Schuster, Evins. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Gilman. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Gilman, Schmitt, Sempio. Obtained funding: Gilman, Evins. Administrative, technical, or material support: Gilman, Schmitt, Wheeler, Klawitter. Supervision: Gilman, Schuster, Evins. Conflict of Interest Disclosures: Drs Gilman and Evins reported having a patent pending (WO 2018/027151) and being cofounders of Brain Solutions, LLC. Dr Schuster reported receiving personal fees from the Massachusetts Department of Public Health and the University of Connecticut outside the submitted work. Dr Sempio reported receiving grants from Massachusetts General Hospital during the conduct of the study. Dr Evins reported receiving grants from the National Institutes of Health and Charles River Analytics during the conduct of the study, personal fees from Pfizer and Karuna Pharmaceuticals, and serving as chair of a data safety monitoring board for a trial. No other disclosures were reported. Funding/Support: This work was supported by grant R01DA042043 from the National Institute on Drug Abuse, National Institutes of Health. Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Additional Contributions: Kevin Potter, PhD (Massachusetts General Hospital), assisted with statistical analyses; he was not compensated for this work beyond his normal salary.

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